fix a typing error

docs/developer-guide/index.md

@@ -609,7 +609,7 @@ All TransmogrifAI Stages can be used as spark ML stages by passing a Dataset or
 
 ## Workflows
 
-Workflows are used to control the execution of the ML pipeline once the final features have been defined. Each Feature contains the history of how it is defined by tracking both the parent Features and the parent Stages. However, this is simply a *description* of how the raw data will be transformed in order to create the final data desired, until the Features are put into a Workflow there is no actual data associated with the pipeline. OpWorkflows create and transform the raw data needed to compute Features fed into them. In addition they optimize the application of Stages needed to create the final Features ensuring optimal computations within the full pipeline DAG. OpWorkflows can be fit to a given dataset using the `.train()` method. This produces and OpWorkflowModel which can then be saved to disk and applied to another dataset.
+Workflows are used to control the execution of the ML pipeline once the final features have been defined. Each Feature contains the history of how it is defined by tracking both the parent Features and the parent Stages. However, this is simply a *description* of how the raw data will be transformed in order to create the final data desired, until the Features are put into a Workflow there is no actual data associated with the pipeline. OpWorkflows create and transform the raw data needed to compute Features fed into them. In addition they optimize the application of Stages needed to create the final Features ensuring optimal computations within the full pipeline DAG. OpWorkflows can be fit to a given dataset using the `.train()` method. This produces an OpWorkflowModel which can then be saved to disk and applied to another dataset.
 
 ### Creating A Workflow